Moon shots: Introducing “True Home Runs” (HitTracker)

If you have your hand up right now, chances are you were mildly to extremely disappointed with these players. Even if you drafted them, looking back, it probably doesn’t look like a poor choice, just one of those instances where the right decision just didn’t pan out. Process is more important than results, and the indicators all pointed to them being good picks. Well, the indicators that were available at the time did, anyway.

Today, I’d like to introduce you to a new stat for predicting home runs that I’ve been working on for quite a while now. This stat would have predicted the decline in the power numbers of each of these players… and many more.

At the end of last year, I discussed some of the ideas I had for using Greg Rybarczyk’s excellent HitTracker system to predict home runs. Greg has helped me for the past few months, and I think we have arrived at a great system for doing just this.

Full explanation of True Home Runs

That article last year discussed why I like the logic behind the No Doubt/Plenty/Just Enough system. To recap, players who hit the ball a long way should also be able to hit the ball a short way. Players who hit a lot of long ones but don’t hit a lot that are just clearing the fence are getting unlucky, while players who don’t hit many long ones but a lot that barely clear the fence are getting lucky.

Keeping this in mind, here is the methodology behind True Home Runs:

Note: If you’d prefer to simply get a quick explanation of this system, that can be found by scrolling down a little bit.

A hitter has an innate ability to hit a baseball with a certain amount of force (or skill, or whatever you wish to call it). This ability, however, is often clouded by circumstances he cannot control. One of the primary intentions of True Home Runs is to eliminate these things.

Weather is one. A hitter who hits a 500-foot home run would generally be considered a great power hitter. But if there is a 75 mph wind blowing out, that probably turned a decent homer (or maybe a warning track fly) into a fantastic moon shot. The hitter doesn’t deserve credit for this. So, in this system, weather is neutralized.

Every homer is run in two environments and given a label (No Doubt, Plenty, Just Enough or not a homer). It is first run in a park with league-average fence dimensions (big thanks to Greg for building this environment), league average elevation above sea level, 70-degree temperature and no wind. Each homer is then run in each hitter’s home park with average weather conditions for that particular park. For parks that play some games with a roof, the homers are run in both environments and weighted according to the percentage of games played in each situation (using data from 2002 to 2006).

While a hitter has this innate ability to hit the ball a certain distance, the park in which he actually does it is out of his control. A neutralized 450-foot home run is a 450-foot home run in Great American Ballpark, the same as it is in PETCO. Therefore, each hitter’s home run total in his home park is weighted the same as his home run total in the league average environment.

What I do when evaluating players (not just now, but in general) is look at the underlying skills and have confidence that the surface numbers will fall into place. In this instance, hitting No Doubt home runs is the skill to be chased. The more No Doubt home runs you are able to hit, the more raw power you presumably have and the more Plenty and Just Enough home runs you should, by default, be able to hit.

So I counted up the number of No Doubt homers each player hit (treating our two scenarios separately) and then figured out the league average percentage of homers that are of the No Doubt variety. In 2006, this was 41 percent in the league average scenario. So for a hitter with 10 No Doubt homers, using this 41 percent average, his True Home Runs total would be 24 (10*100/41).

I didn’t actually take a straight league average because, naturally, players with little power won’t hit the same percentage of No Doubt homers that the elite sluggers will. Some won’t hit any at all and never will. Instead, I broke this down by number of No Doubt homers per outfield fly ball (ND/FB). For example, players who put up at least a 9 percent ND/FB in the league average scenario hit them at a 52 percent clip, clearly above the league average, as we would expect.

For players who didn’t hit any No Doubt homers, this process was done using their Plenty homers and the league average percentage of them.

There might be some selection bias here, and I could end up changing this up a bit; any comments or suggestions on this matter would certainly be taken into consideration.

This is done for each of our two scenarios and each total was weighted at 50 percent and combined to reach a final True Home Run total.

Some homers weren’t tracked by HitTracker, so to avoid putting certain hitters at a disadvantage, their home run total in each scenario was prorated to account for the missing tracked balls.

Short explanation of True Home Runs

Every homer is run through HitTracker in two environments: a league average park with league average weather and the hitter’s home park with average weather for that park. The homers that are given a No Doubt label are counted up and then put into a proportion using the league average percentage of No Doubters.

For example, for a hitter with 10 No Doubt homers, assuming a 41 percent league average, his True Home Runs total would be 24 (10*100/41).

This is done for both environments and we take the average of the two to arrive at a final True Home Run total.

True Home Runs in action

Here are a few players whose power dropoff in 2007 would have been predicted by this system.

To track how True Home Runs predicts the player’s power in the following year, first compare his 2006 actual HR/FB to his 2006 tHR/FB. Then, compare his 2006 tHR/FB to his 2007 actual HR/FB. You’ll notice that there is a large discrepancy between the 2006 HR/FB and 2006 tHR/FB for the players listed, meaning that the player got lucky on the surface. When a player gets lucky, we expect him to regress the following year, as we see happened to all of these guys by looking at their 2007 actual HR/FB.

Note 1: HitTracker didn’t begin tracking data until 2006, the reason for the 0’s for 2004 and 2005.Note 2: LW POWER stands for Linear Weighted Power, which I know some use as a measure of power. It was originally derived by Pete Palmer and is calculated as such:
((2B * .8) + (3B * .8) + (HR * 1.4))/(AB-K)*100.
It is not on the same scale as HR/FB, though it looks like it could be.

Bay was an up-and-coming star and a first-round pick in some fantasy leagues in 2007, but he fell off considerably that year. His 2006 tHR/FB showed that his power had deteriorated before that, though.

Carlos Delgado has long been a top power hitter. Traded to the Mets in 2006, he turned in a very good year. His power was bound to fall off at some point given his age, but I don’t think anyone predicted his HR/FB would fall off nearly 10 points. tHR/FB was close, though.

Lots of people got burned by Hafner last year, I’m sure. All of his power numbers were on the rise, yet he tanked in 2007. It’s a definite possibility that this was caused, in part at least, by an injury or mechanical problem, but tHR/FB predicted a regression past even his 2005 numbers.

Jeter had a HR/FB above 15 in both 2005 and 2006, but his tHR/FB showed that his power had diminished by 2006, and this caught up with him the following year. His raw home run totals were similar in 2006 and 2007 because he cranked his fly ball rate up, possibly pressing having sensed that his raw power was falling off.

Mark Teahen was a youngster on the rise. He hit 18 homers in 2006 despite receiving just 393 at-bats, and I remember one of the toughest competitors I play against drafted him hoping for 30+ homers. Not so fast. tHR/FB didn’t predict as severe a drop-off as Teahen experienced, but it certainly wasn’t fooled by Teahen’s 2006 season.

Bill Hall had a career year in 2006, and his power numbers were all on the rise. tHR/FB showed that his 2006 power was very inflated, though, and he was much closer to the power hitter he was in 2004 than 2006.

While the A’s received a good haul for Swisher this offseason, they definitely waited a year too long to trade him. His HR/FB fell off nearly eight points, and tHR/FB predicted an even greater drop-off. I doubt many of us would have drafted Swisher last year knowing that he was only a 20-home run hitter.

Dye hit a career high 44 homers in 2006 at the age of 32, so we probably didn’t need this system to tell us that he was due for a regression. It did confirm these suspicions and predict the extent of that regression pretty well, though.

The same could be said about McCann. A younger guy, on the rise, suddenly (seemingly) falling off. tHR/FB saw that his power spike in 2006 wasn’t legitimate.

New stats to get acquainted with

Using this system, I’ll be using three new stats that should give us a pretty good idea about a hitter’s power. One will be True Homer Runs (tHR) and True Home Runs per Fly ball (tHR/FB), explained and used above.

I’ll also be using one I call Neutralized Home Runs or Neutralized Power (nHR and nHR/FB). This is simply the number of home runs that would be hit in the league average environment.

Finally, I’ll be using one called Raw Power (RAW). This is a measure of a hitter’s, well, raw power independent of the number of fly balls hit or direction it is hit. It is simply a count of the number of balls hit past 420 feet (roughly the league average distance for No Doubt home runs) in 70-degree weather with no wind per 100 fly balls.

Here’s a sample table of these stats for a hitter who looks like he could be a nice second-half sleeper for those looking for power.

Lind might be in line for more playing time in the second half, and if he gets it, he could rock out as far as his power is concerned. His 19 percent tHR/FB was much higher than his 13 percent actual HR/FB last year, and in limited at-bats this year his HR/FB is 27 percent. This should decrease some, but we see that Lind does have good power.

Concluding thoughts

This system is not perfect yet, and I’m still adjusting to make it better. It is at the point, though, where I have a lot of confidence in the results it produces.

It has been wrong on a few batters, but all systems will be wrong at times, and I like the methodology behind this system more than any other home run projection system I’m familiar with. For those among these hitters that the system missed on a lot, the vast majority saw an increase or decrease in their 2007 tHR/FB that explained why 2006 didn’t predict 2007’s actual HR/FB. Others saw a team switch and a new home park.

And the best part is that this system will only get better once all fly balls start getting tracked. Balls that don’t become home runs due to wind or other weather conditions or a very deep fence aren’t tracked and therefore aren’t included in this system. Their inclusion would make this much, much better. An age curve and regression to the mean would also help, as would a three-year weighted average, which we will be able to do after the 2008 season.

It takes some time to come up with the final numbers on a league-wide basis because of the complexity of all this, but I’ll be running the 2008 numbers at the start of the All-Star break next week. I’ll be taking requests for players you’d like me to look at, and I’ll make an attempt to feature a different player every day that week and maybe even spill over into the following week if there are enough interesting guys to look at. Utley is one who immediately comes to mind. I’ll also be using this in all player analyses.

If you have questions, comments or suggestions for improvement, absolutely feel free to let me know.

On an unrelated note, I apologize for not having a Waiver Wire this week. Between doing all this, the holiday weekend and some other work, there just wasn’t time. We’ll definitely have one this week, though.

Lastly, I’d like to give Greg Rybarczyk one more enormous thank you, for without his vision and his enormous help, this wouldn’t have been possible. Thank you, Greg.